Preprints
https://doi.org/10.5194/egusphere-2022-9
https://doi.org/10.5194/egusphere-2022-9
 
01 Mar 2022
01 Mar 2022
Status: this preprint is open for discussion.

Technical Note: Quantifying Hazard Probability and Risk from Ensemble Projections of Downscaled Climate Variables

James P. Kossin, Timothy M. Hall, and Terence R. Thompson James P. Kossin et al.
  • The Climate Service (an S&P Global company), 110 Corcoran St., Durham, NC, 27701, USA

Abstract. Hazard metrics downscaled from climate model projections are commonly used for assessing future risk and related potential losses at local spatial scales. Quantifying changes in risk into actionable information is essential for building resilience to climate change through adaptation of existing operational assets, siting and design of future assets, identifying transition risks as well as opportunities, determining optimal paths towards net-zero carbon operations, and assessing future cost/benefit ratios for many other current and future actions. In addition to projecting the most-likely, or expected, values for a given hazard, it is important to quantify the probability distribution for that hazard at any specified time in the future. Here we describe a method to incorporate uncertainty in the downscaled hazard metrics to produce a probabilistic forecast, first for any single model, and then for a multi-model ensemble. The uncertainty for any single model represents an estimate of the natural variability of the hazard that is intrinsic to that model, while the uncertainty in the ensemble represents the natural variability of all the models as well as the spread of each model’s projected most-likely value of the hazard. Loss probability can then be determined from the hazard probability via application of impact (or damage) functions that link hazard to loss. The method is first applied to a simple temperature-based hazard variable and then to a multi-climate-variable-based hazard (fluvial flood). More general application procedures are also discussed.

James P. Kossin et al.

Status: open (until 22 May 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-9', Richard Rosen, 05 Mar 2022 reply
    • AC1: 'Reply on CC1', James Kossin, 07 Mar 2022 reply
      • CC2: 'Reply on AC1', Richard Rosen, 07 Mar 2022 reply
        • AC2: 'Reply on CC2', James Kossin, 09 Mar 2022 reply
          • CC3: 'Reply on AC2', Richard Rosen, 09 Mar 2022 reply
            • AC3: 'Reply on CC3', James Kossin, 10 Mar 2022 reply
  • RC1: 'Comment on egusphere-2022-9', Anonymous Referee #1, 09 Apr 2022 reply

James P. Kossin et al.

James P. Kossin et al.

Viewed

Total article views: 598 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
454 118 26 598 7 7
  • HTML: 454
  • PDF: 118
  • XML: 26
  • Total: 598
  • BibTeX: 7
  • EndNote: 7
Views and downloads (calculated since 01 Mar 2022)
Cumulative views and downloads (calculated since 01 Mar 2022)

Viewed (geographical distribution)

Total article views: 569 (including HTML, PDF, and XML) Thereof 569 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 May 2022
Download
Short summary
We describe a method to incorporate uncertainty in downscaled model ensemble projections of climate hazards, and use this uncertainty to create probabilistic projections. The uncertainty comes from two distinct sources: 1) the intrinsic natural variability of each model in the ensemble and 2) the differences, or spread, among the models. Both sources of uncertainty are used to create finite mixture distributions that can be used to form hazard and loss probability for any projected time period.